Martinikorena Aranburu, IonLarumbe Bergera, AndoniAriz Galilea, MikelPorta Cuéllar, SoniaCabeza Laguna, RafaelVillanueva Larre, Arantxa2020-02-052020-10-182020I. Martinikorena, A. Larumbe-Bergera, M. Ariz, S. Porta, R. Cabeza and A. Villanueva, 'Low Cost Gaze Estimation: Knowledge-Based Solutions,' in IEEE Transactions on Image Processing, vol. 29, pp. 2328-2343, 2020. doi: 10.1109/TIP.2019.29464521941-004210.1109/TIP.2019.2946452https://academica-e.unavarra.es/handle/2454/36191Eye tracking technology in low resolution scenarios is not a completely solved issue to date. The possibility of using eye tracking in a mobile gadget is a challenging objective that would permit to spread this technology to non-explored fields. In this paper, a knowledge based approach is presented to solve gaze estimation in low resolution settings. The understanding of the high resolution paradigm permits to propose alternative models to solve gaze estimation. In this manner, three models are presented: a geometrical model, an interpolation model and a compound model, as solutions for gaze estimation for remote low resolution systems. Since this work considers head position essential to improve gaze accuracy, a method for head pose estimation is also proposed. The methods are validated in an optimal framework, I2Head database, which combines head and gaze data. The experimental validation of the models demonstrates their sensitivity to image processing inaccuracies, critical in the case of the geometrical model. Static and extreme movement scenarios are analyzed showing the higher robustness of compound and geometrical models in the presence of user’s displacement. Accuracy values of about 3◦ have been obtained, increasing to values close to 5◦ in extreme displacement settings, results fully comparable with the state-of-the-art.16 p.application/pdfeng© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.Gaze estimation methodsLow resolutionEye trackinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess